Influence maximization (IM) is a challenge in social networks, which depends on the spreader selection. We propose a quadratic programming model to identify a fixed number of initial spreaders to affect the maximum nodes within the minimum diffusion time. We solve this model using a new Distance Aware Spreader Finding (DASF) algorithm independent of the community detection problem. On large-scale social networks, DASF selects anchor nodes by a novel threshold. Then a social distance is defined between anchor nodes via random walk processes. This distance is regularized by the neighborhood degree. Our model finds influential spreaders under the Independent Cascade (IC) diffusion model. It implicitly maximizes the local coverage of spreaders and minimizes the global overlap. We extract the solution of this bi-objective model by finding the principal eigenvector of the regularized distance matrix. Comparing DASF with nine algorithms on various large-scale social networks indicates that DASF performs well based on the influence spread and diffusion rate criteria. The robustness of DASF is also acceptable dealing with different noisy scenarios.

影响最大化（IM）是社交网络中的一个挑战，它取决于传播者的选择。我们提出了一个二次规划模型来识别固定数量的初始传播器，以在最小扩散时间内影响最大节点。我们使用独立于社区检测问题的新距离感知传播器发现 (DASF) 算法来解决该模型。在大规模社交网络上，DASF 通过新的阈值选择锚节点。然后通过随机游走过程定义锚节点之间的社交距离。该距离由邻域度正则化。我们的模型在独立级联 (IC) 扩散模型下找到有影响力的传播者。它隐含地最大化扩展器的局部覆盖并最小化全局重叠。我们通过找到正则化距离矩阵的主特征向量来提取这个双目标模型的解。在各种大型社交网络上将 DASF 与九种算法进行比较表明，DASF 基于影响传播和扩散率标准表现良好。DASF 的鲁棒性在处理不同的嘈杂场景时也是可以接受的。